class_docstr: Inherits time series methods from TSM class.
  
  **** DYNAMIC REGRESSION MODEL ****
  
  Parameters
  ----------
  
  formula : string
      patsy string describing the regression
  
  data : pd.DataFrame
      Field to specify the data that will be used
docstr: Plots forecasts with the estimated model against data
      (Simulated prediction with data)
  
  Parameters
  ----------
  h : int (default : 5)
      How many steps to forecast
  
  fit_once : boolean
      Whether to fit the model once at the beginning (True), or fit every iteration (False)
  
  fit_method : string
      Which method to fit the model with
  
  Returns
  ----------
  - Plot of the forecast against data 
docstr_tok: ["Plots","forecasts","with","the","estimated","model","against","data","(","Simulated","prediction","with","data",")","Parameters","--","--","--","--","--","h",":","int","(","default",":","5",")","How","many","steps","to","forecast","fit_once",":","boolean","Whether","to","fit","the","model","once","at","the","beginning","(","True",")",",","or","fit","every","iteration","(","False",")","fit_method",":","string","Which","method","to","fit","the","model","with","Returns","--","--","--","--","--","-","Plot","of","the","forecast","against","data"]
code: def plot_predict_is(
      self, h=5, fit_once=True, fit_method="MLE", **kwargs
  ):
      """Plots forecasts with the estimated model against data
          (Simulated prediction with data)
  
      Parameters
      ----------
      h : int (default : 5)
          How many steps to forecast
  
      fit_once : boolean
          Whether to fit the model once at the beginning (True), or fit every iteration (False)
  
      fit_method : string
          Which method to fit the model with
  
      Returns
      ----------
      - Plot of the forecast against data
      """
      import matplotlib.pyplot as plt
      import seaborn as sns
  
      figsize = kwargs.get("figsize", (10, 7))
  
      plt.figure(figsize=figsize)
      predictions = self.predict_is(
          h=h, fit_once=fit_once, fit_method=fit_method
      )
      data = self.data[-h:]
      plt.plot(predictions.index, data, label="Data")
      plt.plot(
          predictions.index, predictions, label="Predictions", c="black"
      )
      plt.title(self.y_name)
      plt.legend(loc=2)
      plt.show()
  
imports: import copy
  import sys
  import numpy
  import pandas
  import scipy.stats
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .kalman import *
label_desc: Use image node_id 12 for calling the DynReg obj's underlying member method code with example usage: obj.plot_predict_is(h, fit_once, fit_method) without return types